Mobile relay networks interconnect one or more mobile nodes, a centralized communication system, or base station, and one or more user nodes. Frequently, mobile nodes, or mobile robots, are deployed in physical environments that are uncharted, remote, or inaccessible to conventional measuring techniques. To function most effectively, mobile robots need to discover the properties of the physical environment they are located in. Knowing details of the location can assist navigation, communication, and object retrieval or placement.
Generally, mobile robots use self-contained on-board guidance systems, which can include environmental sensors to track relative movement, detect collisions, identify obstructions, or provide an awareness of the immediate surroundings. Sensor readings are used to plan the next robotic movement or function to be performed. Movement can occur in a single direction or could be a sequence of individual movements, turns, and stationary positions.
Mapping the physical environment can help determine the size of the area explored by the robot, and, if the robot gets stuck or otherwise blocked by an obstacle, allows the robot to return to a known, higher value area. The identification of the physical environment also helps to determine whether the entire area has been traversed, what part of the area has provided better connectivity between the robot, base station, and users, as well as optimize efficient movement of the robot, which maximizes battery life and minimizes time of exploration.
Conventional modes of traversing of the physical environment by robots include using long-range sensors, such as cameras and lasers to detect obstacles in front of or surrounding the robot. Long-range measurement of the environment has a large Overhead, both economically due to the high cost of components and to battery consumption. Additionally, high-level computer cognitive models are used for environment mapping but incur a high computational overhead.
Therefore, there is a need for an approach to identifying features of the physical environment that is cost-effective and efficient. Preferably, such an approach will be able to filter out errors created by obstacles within the physical environment.